293 citations · 420 across the 4 of their papers we have counts for
13 papers
Sentence Boundary Augmentation For Neural Machine Translation Robustness
Daniel Li, Te I, Naveen Arivazhagan +2
Neural Machine Translation (NMT) models have demonstrated strong state of the art performance on translation tasks where well-formed training and evaluation data are provided, but…
Leveraging Monolingual Data with Self-Supervision for Multilingual Neural Machine Translation
Aditya Siddhant, Ankur Bapna, Yuan Cao +5
Over the last few years two promising research directions in low-resource neural machine translation (NMT) have emerged. The first focuses on utilizing high-resource languages to i…
Re-translation versus Streaming for Simultaneous Translation
Naveen Arivazhagan, Colin Cherry, Wolfgang Macherey +1
There has been great progress in improving streaming machine translation, a simultaneous paradigm where the system appends to a growing hypothesis as more source content becomes av…
Controlling Computation versus Quality for Neural Sequence Models
Ankur Bapna, Naveen Arivazhagan, Orhan Firat
Most neural networks utilize the same amount of compute for every example independent of the inherent complexity of the input. Further, methods that adapt the amount of computation…
Re-Translation Strategies For Long Form, Simultaneous, Spoken Language Translation
Naveen Arivazhagan, Colin Cherry, Te I +3
We investigate the problem of simultaneous machine translation of long-form speech content. We target a continuous speech-to-text scenario, generating translated captions for a liv…
Simple, Scalable Adaptation for Neural Machine Translation
Ankur Bapna, Naveen Arivazhagan, Orhan Firat
Fine-tuning pre-trained Neural Machine Translation (NMT) models is the dominant approach for adapting to new languages and domains. However, fine-tuning requires adapting and maint…